Higher Ground? How Groundtruth Labeling Impacts Our Understanding of Fake News about the 2016 U.S. Presidential Nominees

COVID-19 vaccine hesitancy has increased concerns about vaccine uptake required to overcome the pandemic and protect public health. A critical factor associated with anti-vaccine attitudes is the information shared on social media. In this work, we investigate misinformation communities and narratives that can contribute to COVID-19 vaccine hesitancy. During the pandemic, anti-science and political misinformation/conspiracies have been rampant on social media. Therefore, we investigate misinformation and conspiracy groups and their characteristic behaviours in Twitter data collected on COVID-19 vaccines. We identify if any suspicious coordinated efforts are present in promoting vaccine misinformation, and find two suspicious groups - one promoting a ‘Great Reset’ conspiracy which suggests that the pandemic is orchestrated by world leaders to take control of the economy, with vaccine related misinformation and strong anti-vaccine and anti-social messages such as no lock-downs; and another promoting the Bioweapon theory. Misinformation promoted is largely from the anti-vaccine and far-right communities in the 3-core of the retweet graph, with its tweets proportion of conspiracy and questionable sources to reliable sources being much higher. In comparison with the mainstream and health news, the right-leaning community is more influenced by the anti-vaccine and far-right communities, which is also reflected in the disparate vaccination rates in left and right U.S. states. The misinformation communities are also more vocal, either in vaccine or other discussions, relative to remaining communities, besides other behavioral differences. Furthermore, we investigate the COVID-19 vaccine narratives spread on social media. Besides misinformation narratives about vaccine safety, effectiveness and conspiracies, we find that rarer vaccine side-effects, reported less frequently in CDC VAERS reports, were more frequently discussed on social media, and in misinformation narratives, which also use other known tactics of science narratives distortion.

Section: Data Collectionmentioning

confidence: 99%

COVID-19 Vaccine Misinformation Campaigns and Social Media Narratives

Sharma

Zhang

Liu

2022

“…These computerbased approaches have shown remarkable success, leveraging signals such as sharing patterns (Rosenfeld, Szanto, and Parkes 2020), text features (Granik and Mesyura 2017), account activity (Breuer, Eilat, and Weinsberg 2020), user stance (Weinzierl, Hopfer, and Harabagiu 2021) and vi-sual features for website screenshots (Abdali et al 2021). However, the nuanced nature of truth, the limited availability of labeled training data (Rubin, Chen, and Conroy 2015;Bozarth, Saraf, and Budak 2020), and the nonstationarity problem whereby the signatures of misinformation can change rapidly (e.g. with the rise of COVID-19 misinformation) place fundamental limits of the effectiveness of fully automated systems.…”

Section: Approaches To Detecting Misinformationmentioning

confidence: 99%

Do Explanations Increase the Effectiveness of AI-Crowd Generated Fake News Warnings?

Epstein

Foppiani

Hilgard

et al. 2022

Social media platforms are increasingly deploying complex interventions to help users detect false news. Labeling false news using techniques that combine crowd-sourcing with artificial intelligence (AI) offers a promising way to inform users about potentially low-quality information without censoring content, but also can be hard for users to understand. In this study, we examine how users respond in their sharing intentions to information they are provided about a hypothetical human-AI hybrid system. We ask i) if these warnings increase discernment in social media sharing intentions and ii) if explaining how the labeling system works can boost the effectiveness of the warnings. To do so, we conduct a study (N=1473 Americans) in which participants indicated their likelihood of sharing content. Participants were randomly assigned to a control, a treatment where false content was labeled, or a treatment where the warning labels came with an explanation of how they were generated. We find clear evidence that both treatments increase sharing discernment, and directional evidence that explanations increase the warnings' effectiveness. Interestingly, we do not find that the explanations increase self-reported trust in the warning labels, although we do find some evidence that participants found the warnings with the explanations to be more informative. Together, these results have important implications for designing and deploying transparent misinformation warning labels, and AI-mediated systems more broadly.

“…Polarization and Political Bias. Many papers have recently been published on detecting political bias of online content either automatically (Baly et al 2020;Demszky et al 2019) or manually (Ganguly et al 2020;Bozarth, Saraf, and Budak 2020). Others have examined bias in moderation of content, as opposed to biased content or news sources themselves Wilson 2019, 2020).…”

Section: Introductionmentioning

confidence: 99%

Political Bias and Factualness in News Sharing across more than 100,000 Online Communities

Weld

Glenski

Althoff

2021

As civil discourse increasingly takes place online, misinformation and the polarization of news shared in online communities have become ever more relevant concerns with real world harms across our society. Studying online news sharing at scale is challenging due to the massive volume of content which is shared by millions of users across thousands of communities. Therefore, existing research has largely focused on specific communities or specific interventions, such as bans. However, understanding the prevalence and spread of misinformation and polarization more broadly, across thousands of online communities, is critical for the development of governance strategies, interventions, and community design. Here, we conduct the largest study of news sharing on reddit to date, analyzing more than 550 million links spanning 4 years. We use non-partisan news source ratings from Media Bias/Fact Check to annotate links to news sources with their political bias and factualness. We find that, compared to left-leaning communities, right-leaning communities have 105% more variance in the political bias of their news sources, and more links to relatively-more biased sources, on average. We observe that reddit users’ voting and re-sharing behaviors generally decrease the visibility of extremely biased and low factual content, which receives 20% fewer upvotes and 30% fewer exposures from crossposts than more neutral or more factual content. This suggests that reddit is more resilient to low factual content than Twitter. We show that extremely biased and low factual content is very concentrated, with 99% of such content being shared in only 0.5% of communities, giving credence to the recent strategy of community-wide bans and quarantines.